BBC News Article Research Articles

Automatic Extractive Text Summarization using Multiple Linguistic Features

Automatic text summarization (ATS) provides a summary of distinct categories of information using natural language processing (NLP). Low-resource languages like Hindi have restricted applications of these techniques. This study proposes a method for automatically generating summaries of Hindi documents using extractive technique. The approach retrieves pertinent sentences from the source documents by employing multiple linguistic features and machine learning (ML) using maximum likelihood estimation (MLE) and maximum entropy (ME). We conducted pre-processing on the input documents, such as eliminating Hindi stop words and stemming. We have obtained 15 linguistic feature scores from each document to identify the phrases with high scores for summary generation. We have performed experiments over BBC News articles, CNN News, DUC 2004, Hindi Text Short Summarization Corpus, Indian Language News Text Summarization Corpus, and Wikipedia Articles for the proposed text summarizer. The Hindi Text Short Summarization Corpus and Indian Language News Text Summarization Corpus datasets are in Hindi, whereas BBC News articles, CNN News, and the DUC 2004 datasets have been translated into Hindi using Google, Microsoft Bing, and Systran translators for experiments. The summarization results have been calculated and shown for Hindi as well as for English to compare the performance of a low and rich-resource language. Multiple ROUGE metrics, along with precision, recall, and F-measure, have been used for the evaluation, which shows the better performance of the proposed method with multiple ROUGE scores. We compare the proposed method with the supervised and unsupervised machine learning methodologies, including support vector machine (SVM), Naive Bayes (NB), decision tree (DT), latent semantic analysis (LSA), latent Dirichlet allocation (LDA), and K-means clustering, and it was found that the proposed method outperforms these methods.

LeanContext: Cost-efficient domain-specific question answering using LLMs

Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, widespread LLM integration presents a challenge for small businesses due to the high expenses of LLM API usage. Costs rise rapidly when domain-specific data (context) is used alongside queries for accurate domain-specific LLM responses. Extracting context from domain-specific data is implemented by a Retrieval Augmented Generation (RAG) approach. One option is to summarize the RAG context by using LLMs and reduce the context. However, this can also filter out useful information that is necessary to answer some domain-specific queries. In this paper, we shift from human-oriented summarizers to AI model-friendly summaries. Our approach, LeanContext, efficiently extracts k key sentences from the context that are closely aligned with the query. The choice of k is neither static nor random; we introduce a reinforcement learning technique that dynamically determines k based on the query and context. The rest of the less important sentences are either reduced using a free open-source text reduction method or eliminated. We evaluate LeanContext against several recent query-aware and query-unaware context reduction approaches on prominent datasets (arxiv papers and BBC news articles, NarrativeQA). Despite cost reductions of 37.29% to 67.81%, LeanContext’s ROUGE-1 score decreases only by 1.41% to 2.65% compared to a baseline that retains the entire context (no summarization). LeanContext stands out for its ability to provide precise responses, outperforming competitors by leveraging open-source summarization techniques. Human evaluations of the responses further confirm and validate this superiority. Additionally, if open-source pre-trained LLM-based summarizers are used to reduce context (into human consumable summaries), LeanContext can further modify the reduced context to enhance the accuracy (ROUGE-1 score) by 13.22% to 24.61%.

TYPES OF DERIVATIONAL AFFIXES FOUND IN BBC NEWS ARTICLES

This study entitled types of derivational affixes found in BBC News articles. The data in this study are the intricate words found in some of the BBC News articles. The purpose of this study is to concentrate on exploring the types of the derivational affixes from the data found in some of the BBC News articles. This study used qualitative and observation methods to analyze the data. The data in this study were identified using the main theory offered by Katamba (1993) and supporting theory by Carstairs and McCarthy (2002) proposed to solve the topic presented in this study. The result of this study is that 100 data can be found from BBC News articles that containing derivational affixes. In this research, prefixes and suffixes are the two forms of derivational affixes. This study found 7 types of prefixes with the value of 12% such as re-, al-, en-, un-, im-, ir-, dis-. Therefore, there are 17 types of suffixes that found in the data source with the value of 88% such as -ous, -ment, -ly, -tion, -al, -en, -able, -ness, -y, -ence, -er, -ful, -less, -ity, -ship, -ive, and -ation.

Feature Discrimination of News Based on Canopy and KMGC-Search Clustering

The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means Clustering and Spectral Clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with K-means clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

SYNTACTIC ANALYSIS OF SENTENCE PATTERNS AND TYPES OF BBC NEWS ARTICLES

Sentence patterns and types play a crucial role in helping writers make their articles attractive. However, few studies analyzed the sentence patterns and types used in news articles. Therefore, this study aimed to explore the sentence patterns and types used in shaping news articles. The researchers used a syntactic analysis to examine the sentences. The data were collected from ten BBC news articles published in October 2020. The purposive sampling was used, and the data gathering instrument was structured observation sheets. The gathered data were calculated using percentages. The results showed five sentence patterns used, namely S+V, S+LV+SC, S+V+O, S+V+DO+OC, and S+V+IO+DO. It was also found that four sentence types, namely simple, compound, complex, and compound-complex sentences, were used. Among the used sentence patterns, S+V+O was the most frequent (42%), and the most frequent sentence type was the complex sentence (52.6%).

Investigating Category Translation Shifts of a BBC News Article from English into Arabic

Investigating translation shifts in the translation of news articles has not received much attention despite its paramount importance in how translators produce adequate translations. Indeed, news translation is considered to be understudied in the field of translation. The current research aims at identifying the types of category shifts that have occurred and the most employed ones in the selected corpus.This study offers also an understanding of the category shifts that occur concerning translating news reports or stories. Accordingly, the study will explore the translation of a news article from English into Arabic; the news article titled ‘North Korea ‘preparing rocket launch’, images suggest’ is published on BBC news website 9 March 2019 together with its translation which was found on the BBC Arabic news website. To attain the research objectives, a comparative investigation of the English source text and the Arabic target text will be needed to look into the use of category shifts following Catford’s typology of category shifts. This study adopted a qualitative and quantitative method in identifying what kinds of category shifts occurred and the most frequent ones in the analyzed news text. The findings show that all types of category shifts were employed in the translation with a total of 35 shifts. The most employed type of category shift is structural shifts which are exemplified through 16 cases. These structural shifts are illustrated in word-order, passive-active, or nominal-verbal sentence structures. The least applied category shifts in the process of translation are class shifts which indicate that the translator found target text equivalences that have the same part of speech of the source text items. The study concludes that category shifts were employed in the translation to fill the linguistic gap between the two languages.

Investigating Category Translation Shifts of a BBC News Article from English into Arabic

Investigating translation shifts in the translation of news articles has not received much attention despite its paramount importance in how translators produce adequate translations. Indeed, news translation is considered to be understudied in the field of translation. The current research aims at identifying the types of category shifts that have occurred and the most employed ones in the selected corpus.This study offers also an understanding of the category shifts that occur concerning translating news reports or stories. Accordingly, the study will explore the translation of a news article from English into Arabic; the news article titled ‘North Korea ‘preparing rocket launch’, images suggest’ is published on BBC news website 9 March 2019 together with its translation which was found on the BBC Arabic news website. To attain the research objectives, a comparative investigation of the English source text and the Arabic target text will be needed to look into the use of category shifts following Catford’s typology of category shifts. This study adopted a qualitative and quantitative method in identifying what kinds of category shifts occurred and the most frequent ones in the analyzed news text. The findings show that all types of category shifts were employed in the translation with a total of 35 shifts. The most employed type of category shift is structural shifts which are exemplified through 16 cases. These structural shifts are illustrated in word-order, passive-active, or nominal-verbal sentence structures. The least applied category shifts in the process of translation are class shifts which indicate that the translator found target text equivalences that have the same part of speech of the source text items. The study concludes that category shifts were employed in the translation to fill the linguistic gap between the two languages.

Особенности и трудности перевода эвфемизмов (на материале новостных статей BBC)

Особенности и трудности перевода эвфемизмов (на материале новостных статей BBC)

Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

Due to advancement in technology, enormous amount of data is generated every day. One of the main challenges of large amount of data is user overloaded with huge volume of data. Hence effective methods are highly required to help user to comprehend large amount of data. This research work proposes effective methods to extract and represent the data. The summarization is applicable to obtain a brief overview of the text and sentiment analysis can obtain emotions expressed in the text computationally. The combined text summarization and sentiment analysis is proposed on BBC news articles. A pronoun replacement based text summarization method is developed and VADER sentiment analyzer is used to determine sentiment information. The 3-D visualization schemes have been provided to represent the sentiment information. The sentiment analysis and classification are performed on original BBC news articles as well as on summarized articles using classifiers, such as Logistic Regression, Random Forest and Adaboost. On original news articles highest classification rate of 84.93%, using summarization of ratio 25%, 50% and 75% highest classification rates of 78.73%, 83.06% and 83.23%, respectively are observed.

ABCD: agent based model for document classification

Document classification is the task of analysing, identifying and categorising collection of documents into their annotated classes based on their contents. This paper presents ABCD as an agent-based classifier for documents. ABCD is autonomous by depending on software agents in collecting and distributing documents, and smart by exploiting machine learning techniques to train the underlying classifier. As such, the system consists of two essential components, namely, the agent component and the classification component. To be comprehensive and to facilitate comparative results, five statistical classifiers are exploited. These classifiers are based on Naive Bayes (NB), Hidden Markov Model (HMM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Support Vector Machine (SVM) and Random Forest (RF) algorithms. The proposed model is experimentally tested on both BBC news articles dataset and News Aggregator dataset from artificial intelligence lab. The obtained results indicate the superiority of the Random Forest algorithm for classifying unimodal documents.

Everyday climate discourses and sustainable tourism

ABSTRACTDebates surrounding the human impact on climate change have, in recent years, proliferated in political, academic, and public rhetoric. Such debates have also played out in the context of tourism research (e.g. extent to which anthropogenic climate change exists; public understanding in relation to climate change and tourism). Taking these debates as its point of departure, whilst also adopting a post-structuralist position, this paper offers a Foucauldian Discourse Analysis of comments to an online BBC news article concerning climate change. Our analysis finds three key ways responsibility is mitigated through climate change talk: scepticism towards the scientific evidence surrounding climate change; placing responsibility on the “distant other” through a nationalistic discourse; and presenting CO2 as “plant food”. The implications of these ways of thinking about climate change are discussed with a focus on how this translates into action related to the sustainability of tourism behaviours. In doing so, it concludes that a deeper understanding of everyday climate talk is essential if the tourism sector is to move towards more sustainable forms of consumption.